In many information processing systems, service event analysis relies heavily on inefficient manual activities. For example, it is common in some systems for service personnel to be required to complete forms describing problems experienced by customers and the manner in which these problems were resolved. These forms often utilize static sets of predetermined problem and resolution codes that, in the interest of convenience to the service personnel, tend to be overly general and vague.
Supplementary unstructured text data added to such forms is often ignored as it requires special treatment. For example, the unstructured text data may require manual screening in which a corpus of unstructured text data is reviewed and sampled by service personnel. Alternatively, the unstructured text data may require manual customization and maintenance of a large set of rules that can be used to determine correspondence with predefined themes of interest. Such processing is unduly tedious and time-consuming, particularly for large volumes of unstructured text data.
As a result of these and other difficulties in processing unstructured text data associated with service events, such unstructured text data is generally not taken into account in typical conventional implementations of system functions such as selection of subject matter experts for handling service requests. Conventional expert selection therefore often relies on expert skills databases or rules-based selection approaches that involve significant manual intervention and are unduly burdensome to maintain.